Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals

TL;DR

This paper introduces a novel deep CNN model that automatically detects mind wandering from EEG signals, achieving high accuracy and sensitivity, which can enhance attention-aware interfaces.

Contribution

It is the first study to employ CNN for automatic mind wandering detection solely using EEG data, advancing neurotechnology applications.

Findings

Achieved 91.78% accuracy in MW detection

Demonstrated 92.84% sensitivity and 90.73% specificity

First CNN-based approach for EEG-only MW detection

Abstract

Mind wandering (MW) is a ubiquitous phenomenon which reflects a shift in attention from task-related to task-unrelated thoughts. There is a need for intelligent interfaces that can reorient attention when MW is detected due to its detrimental effects on performance and productivity. In this paper, we propose a deep learning model for MW detection using Electroencephalogram (EEG) signals. Specifically, we develop a channel-wise deep convolutional neural network (CNN) model to classify the features of focusing state and MW extracted from EEG signals. This is the first study that employs CNN to automatically detect MW using only EEG data. The experimental results on the collected dataset demonstrate promising performance with 91.78% accuracy, 92.84% sensitivity, and 90.73% specificity.